residues of antimicrobial agents and related compounds of...
TRANSCRIPT
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Residues of antimicrobial agents and related compounds of emerging concern in manure, water and soil
Part 2 – Final data set
of a pilot campaign and
outline for an EU-wide
monitoring assessment
Gawlik BM, Mariani G, Glowacka N,
Gadus J, Skejo H, Comero S, Tavazzi S
2018
EUR 29065 EN
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This publication is a Technical report by the Joint Research Centre (JRC), the European Commission’s science
and knowledge service. It aims to provide evidence-based scientific support to the European policymaking
process. The scientific output expressed does not imply a policy position of the European Commission. Neither
the European Commission nor any person acting on behalf of the Commission is responsible for the use that
might be made of this publication.
Contact information
Name: Bernd Manfred Gawlik
Address: European Commission, Joint Research Centre, Via Enrico Fermi 2749, 21027 Ispra (Va), Italy
Email: [email protected]
Tel.: +39 0332 78 9487
JRC Science Hub
https://ec.europa.eu/jrc
JRC113289
EUR 29065 EN
Print ISBN 978-92-79-96691-0 ISSN 1018-5593 doi:10.2760/07151
PDF ISBN 978-92-79-96693-4 ISSN 1831-9424 doi:10.2760/280841
Luxembourg: Publications Office of the European Union, 2018
© European Union, 2018
Reuse is authorised provided the source is acknowledged. The reuse policy of European Commission documents is regulated by Decision 2011/833/EU (OJ L 330, 14.12.2011, p. 39).
For any use or reproduction of photos or other material that is not under the EU copyright, permission must be
sought directly from the copyright holders.
How to cite this report: Gawlik BM, Mariani G, Glowacka N, Gadus J, Skejo H, Comero S, Tavazzi S, Residues of
antimicrobial agents and related compounds of emerging concern in manure, water and soil. Part 2 – Final data
set of a pilot campaign and outline for an EU-wide monitoring assessment, EUR 29065 EN, Publications Office of
the European Union, Luxembourg, 2018, ISBN 978-92-79-96693-4, doi:10.2760/280841, PUBSY No.
JRC113289
All images © European Union 2018.
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Contents
Acknowledgements ................................................................................................ 5
Abstract ............................................................................................................... 6
1. Introduction ...................................................................................................... 7
2. Multi-compound analytical methodology ............................................................. 10
2.1.1. Cattle urine for polar compounds extraction ........................................... 10
2.1.2. Cattle urine for apolar compounds extraction ......................................... 10
2.1.3. Non-processed manure for polar compounds extraction .......................... 11
2.1.4. Non-processed manure for apolar compounds extraction ......................... 11
2.1.5. Processed manure .............................................................................. 11
2.1.6. Soil ................................................................................................... 11
2.1.7. Water for polar and apolar substances .................................................. 12
2.2. Analytical methods ............................................................................... 13
2.2.1. UHPLC-MS/MS ............................................................................... 13
2.2.2. GC-IonTrap-MS .................................................................................. 13
2.3. Analytical results ................................................................................. 14
2.3.1. Results of cattle urine analysis ............................................................. 14
2.3.2. Results of non-processed manure analysis ............................................. 15
2.3.3. Results of processed manure analysis ................................................... 16
2.3.4. Results of soil analysis ........................................................................ 17
2.3.5. Results of water samples analysis .................................................... 18
2.4. Discussion .......................................................................................... 22
2.4.1. Comparison between LC-MS/MS and GC-IonTrap-MS .............................. 22
2.4.2. HRGC-HRMS confirmation analysis of GC-Ion Trap-MS results.............. 24
2.4.3. HRGC-HRMS confirmation analysis of GC-Ion Trap-MS results.............. 25
2.4.4. Observed occurrences and levels...................................................... 28
2.4.5. Qualitative PCA .............................................................................. 37
3. Design for an EU-wide assessment .................................................................... 39
3.1. Material characterisation ....................................................................... 39
3.2. Environmental behaviour ...................................................................... 40
4. Conclusions .................................................................................................... 41
References ......................................................................................................... 42
List of abbreviations and definitions ....................................................................... 45
Disclaimer .......................................................................................................... 46
List of figures ...................................................................................................... 47
List of tables ....................................................................................................... 48
Annex – Supplementary information ...................................................................... 49
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Foreword
The Laboratory of the Water and Marine Resources Unit investigates the occurrence and
fate of chemical pollutants entering and travelling with the natural and urban water
cycles. In doing so, the laboratory also characterises the possible treatment and removal
options for such compounds. In particular the so-called Compounds of Emerging Concern
(CECs) as well as their degradation and metabolisation products are of interest in its
investigation.
The issue of veterinary medicinal products and in particular those of an antimicrobial
effect have attracted interest while trying to understand the development and
propagation of antimicrobial resistances.
Likewise, the recovery of resources and energy from waste related to animal husbandry,
e.g. manures, slurries and alike is evolving into an important practice under the EU's
Strategy for a Circular Economy. To steer a sustainable development into the right
direction, it is important not only to understand the opportunities that arrive from a
circular economy approach but also to manage the related circularity of risks related to
it.
In order to improve the knowledge base the laboratory carries out an EU-wide
assessment on processed manures as well as on waters exposed directly or indirectly to
manure and derived fertilising products. Particular attention is given to the investigation
of agricultural runoff, but also to the question to which extend such chemicals will enter
either the food chain or other supply chains in case of reuse of the manure.
The findings are published in a series of technical reports in which this one is the second
stepping-stone in building an enhanced knowledge base for the making and
implementation of improved EU policies.
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Acknowledgements
The support of the technical services and staff of the Slovak University of Agriculture in
Nitra, Faculty of European Studies and Regional Development, Department of Regional
Bioenergy is gratefully acknowledged.
Authors
Bernd Manfred Gawlik (corresponding author)
European Commission, Joint Research Centre, Directorate Sustainable Resources, Water
and Marine Resources Unit, Via Enrico Fermi 2749, I-21027 Ispra (Va), Italy, Email:
Giulio Mariani
European Commission, Joint Research Centre, Directorate Sustainable Resources, Water
and Marine Resources Unit, Via Enrico Fermi 2749, I-21027 Ispra (Va), Italy, Email:
Natalia Głowacka
Slovak University of Agriculture in Nitra, Faculty of European Studies and Regional
Development, Department of Regional Bioenergy, Tr. A. Hlinku 2, SK-94976 Nitra, Slovak
Republic: Email: [email protected].
Environmental Institute, Okruzna 784/42, SK-97241 Kos, Slovak Republic.
Ján Gaduš
Slovak University of Agriculture in Nitra, Faculty of European Studies and Regional
Development, Department of Regional Bioenergy, Tr. A. Hlinku 2, SK-94976 Nitra, Slovak
Republic: Email: [email protected].
Helle Skejø
European Commission, Joint Research Centre, Directorate Sustainable Resources, Water
and Marine Resources Unit, Via Enrico Fermi 2749, I-21027 Ispra (Va), Italy, Email:
Sara Comero
European Commission, Joint Research Centre, Directorate Sustainable Resources, Water
and Marine Resources Unit, Via Enrico Fermi 2749, I-21027 Ispra (Va), Italy, Email:
Simona Tavazzi
European Commission, Joint Research Centre, Directorate Sustainable Resources, Water
and Marine Resources Unit, Via Enrico Fermi 2749, I-21027 Ispra (Va), Italy, Email:
mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]
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Abstract
In a thinking of circular economy, the understanding of how problematic chemical
substances may migrate and travel across the various boundaries of a life-cycle, is of
pivotal importance to ensure that the philosophy of reuse and recycle is not jeopardized
by new risks of contamination. Recycled nutrients from animal manure and slurry can
replace nutrients from primary raw materials. The main challenge is to obtain recycled
nutrient resources that have an equal or better environmental performance than the
primary nutrient resources they replace. In this framework, veterinary medicinal
products (VMP) and in particular the anti-microbial agents (AMAs) are a growing source
of concern in the context of the reuse of processed manure as a fertilizer.
In order to prepare a larger and EU-wide monitoring exercise aiming at the
characterisation of processed manure as well as on the waters exposed directly or
indirectly to the (processed) manure, a first pilot exercise was organised to develop an
appropriate protocol. While the first related report compiled a series of background
information collected, the results on the analytical characterization of pilot sites operated
by the Slovak University of Agriculture in Nitra are presented and discussed here.
Manure samples (processed and untreated), runoff, groundwater and surface water
samples, were analysed for 488 compounds covering typical representatives of
herbicides, fungicides, insecticides, pharmaceuticals, ingredients of personal care
products and other industrially used chemicals. For 60 of these compounds
(corresponding to 12 %), concentration above the established limits of quantification of
these novel multi-compound technique were obtained.
The study demonstrates the applicability of the hybrid target / non-target analytical
approach called "Compound Fishing" and the reports presents the design for a related
EU-wide exercise. Although this study does not characterize the respective test sites, it
delivers an understanding of environmental pressures created on sites and under real-
field scenarios. The experimental work conducted allows also to establish a link between
the evaluation of scientific literature, the biogeochemical modelling and the field
conditions scenarios of when processed manure is applied.
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1. Introduction
There is growing evidence that both, human and veterinary medicines are harmful to
wildlife and ecosystem (Sebestyén et al., 2018). Thus anti-parasites can be excreted and
affect adversely organisms living in or feed on dung (Liebig et al., 2010). Charuaud et al.
(2019) reviewed studies from 2007 to 2017 and identified sixty-eight veterinary
pharmaceutical residues in natural waters reaching up concentrations as high as several
micrograms per litre. While a great deal of information could be found on antibiotics, the
authors reported a data gap on occurrence and fate of anti-parasitic drugs with special
focus on tap waters.
Sandoz et al. (2018) provide an up-to-date review on most of the traditional
dissemination pathways of pharmaceuticals used for veterinary purposes, including
prophylaxis and growth promotion with focus on beef cattle farmyards. While transport of
these compounds are generally investigated in relationship to manure management and
soil application as well as the processes involved, the authors suggest that aerial
transport and deposition may play a significant role, too, in particular in arid and semi-
regions.
Consequently, Christou et al. (2018) suggest considering and studying these group of
pharmaceuticals as an emerging risk with regard to phytotoxicity (largely unknown) and
for agricultural sustainability, mainly due to the uncertainties related to combined effects
in the chemical mixtures. Indeed, the review from Bottoni and Caroli (2018) on some
1000 papers, reports and other publicly available documents covering a three-year
period (2014-2016) raises serious concern about the impact of pharmaceutical residues
amongst others on food commodities. Among the different groups of compounds of
emerging concern related to animal husbandry, it is obvious that veterinary antibiotics
are of primary concern. Used in large scale and quantities, these substances are usually
poorly sorbed in the animal gut and hence largely excreted unchanged or poorly
metabolised in faeces and urine, making them an agro-ecological issue of planetary
relevance (Kuppusamy et al., 2018 and literature cited). The same review identifies the
use of animal manure/urine either directly or after processing as primary cause for the
release of veterinary antibiotics into the agro-ecosystem. Grenni and co-worker (2018)
present well the current state of knowledge regarding the ecological effect of antibiotics
on natural ecosystems with special focus on natural microbial communities in soil and
water. Riaz et al. (2018) concentrated in their critical review on fluoroquinolones.
There is abundant evidence of the complex, yet clear relationship between the
occurrence of antimicrobial resistances (AMR) and the use of antimicrobial agents.
Scenarios forecast for a not too far future are indeed alarming. Thus, in 2016, de Kraker
et al. (2016) estimated for the year 2050 a 10 000 000 annual deaths due to
antimicrobial resistances. The same authors yet acknowledge the unreliability of such
global estimates of the burden of AMR and called for detailed and reliable data
“preferably based on comprehensive, population-based surveillance data from low-,
middle-, and high-income countries.” In his commentary published in The Lancet,
Asaduzzaman (2018) repeats his call for action and the need of a globally coordinated
approach to tackle the antimicrobial resistance challenge, involving also an
environmental mitigation strategy.
25 000 patients die annually in the EU alone as a result of infections caused by resistant
bacteria and globally this number could be as high as 700 000 (EC,
http://ec.europa.eu/health/amr/sites/amr/files/amr_factsheet_en.pdf ). According to the
same source, the bulk of antimicrobials are not consumed by humans, but by animals. In
the US, the livestock sector accounts for about 80 % of total annual consumption.
Between 2010 and 2030, global consumption of antimicrobials in the livestock sector is
projected to increase by about 67 %.
According to an EMA ESVAC Report (2016), of the overall sales of antimicrobials in the
29 countries in 2014, the largest amounts, expressed as a proportion of mg/PCU, were
accounted for tetracyclines (33.4 %), penicillins (25.5 %) and sulfonamides (11.0 %).
http://ec.europa.eu/health/amr/sites/amr/files/amr_factsheet_en.pdf
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From the antimicrobial classes listed in the third World Health Organization (WHO) list of
critically important antimicrobials (CIAs) with the highest priority in human medicine, the
sales for food-producing animals of 3rd- and 4th-generation cephalosporins,
fluoroquinolones and macrolides accounted for 0.2 %, 1.9 % and 7.5 %, respectively, of
the total sales in the 29 countries participating in ESVAC in 2014. Overall, the sales of
polymyxins (mg/PCU) accounted for 6.6 % of the total sales, with colistin representing
more than 99 % of the sales of polymyxins. In other words, animal husbandry is an
important source for antimicrobial substances reaching the environment.
As mentioned above, animal manure and urine play a crucial role in the release of
veterinary medicinal agents and in particular antibiotics to the agro-environment. Due to
the aforementioned poor sorption in the animal gut, active ingredients may be excreted
even up to 90 percent, but may feature also much better uptake leading to release rates
of below 5 % (Spielmeyer, 2018). The subsequent use of manure prior to application,
e.g. as substrate in biogas plants, as well as enhanced re-valorisation techniques
obviously alter the concentration of these substances.
Likewise, their presence may however also affect the productivity of the plant, e.g. by
decreasing the microbial activity in a digester (Spielmeyer, 2018; Zhang et al., 2019;
Ezzariai et al., 2018; Berendsen et al., 2018; Bousek et al., 2018). Bousek et al. (2018)
postulate consequently a combination of different treatment processes for high antibiotic
reduction and reported that unaccustomed anaerobic digestion is inhibited at low
antibiotic concentrations. Fu et al. (2018) quantified the inhibition of an anaerobic
manure digestion process through the presence of tetracycline. According to their
findings, tetracycline presence leads to an 8.8 % of yield reduction for methane in the
investigated AD reactor, an effect however, which was reduced to 4.8 % by the addition
of calcium peroxide. The optimisation of degradation processes occurring to the
anaerobic digestion step, was subject on another study aiming at amoxicillin (Liu et al.,
2018). Their results suggested a rather rapid degradation to this compound and its
breakdown products.
The release of ammonia and the production of volatile fatty acids (VFA) are important in
the process optimisation of high-solid anaerobic digestion. According to Sui et al. (2018
and literature cited), indeed, there is not only a relationship between the effect of
exogenous inhibitors such as ammonia and VFA and the efficiency of the anaerobic
digestion, but also on the occurrence of antibiotic resistance in digested pig manure.
In this setting, Spielmeyer (2018) draw the attention to the fact that although apparently
high removal rates may be achievable through processing, degradation cannot be
assumed automatically, since in many cases only minor structural modification of the
parent substance led to still microbiologically active molecules.
Berendsen et al. (2018) investigated 46 different antibiotics in fortified manure samples
after 24 days of incubation of manure from calves, pigs and broilers observing cases of
persistence of up to a year in some cases.
Mullen et al. (2019) investigated plant uptake by Zea mays L. grown on manure-fertilized
soil. Although tetracyclines tended to predominate in soil, a significantly larger plant
bioaccumulation was observed for sulphonamides. However, the authors also confirmed
that in none of the plant samples antimicrobial resistance genes were observed.
Albero and co-workers (2018) examined more in detail the relationship between
persistence and availability of selected veterinary antibiotics in soil and soil-
(poultry)manure systems concluding that the route of entry into the soil system, e.g. use
of recycled water vs. manure application may play an important role as regards the soil
availability of the such compounds. Thus, among the studied compounds
sulfamethoxazole, sulfamethazine and lincomycin featured the highest soil availability,
while levels of chlortetracycline, doxycycline, ciproflaxin and enrofloxacin in the soil
aqueous was very low.
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In a plant toxicity study, Sidhu et al. (2019a) assessed ciprofloxacin and azithromycin
with regard to their transfer from biosolid-amended soil to three plants (radish –
Raphanus sativus, lettuce – Lactuca sativa, tall fescue grass – Festuca arundinacea).
According to their conclusions only minimal risks to plants and possible impact on human
and/or the animal food chain exist. In a related study (Sidhu et al., 2019b) ascribed this
limited bioavailability to stronger adsorption to the organic matter of the biosolid.
The comparative analysis of 40 samples of cattle, pig and poultry manure, 65 related soil
samples and 27 vegetation samples in an agricultural area in North-western Spain,
however, detected significant number of antibiotic residues in 71 % of grass and corn
samples (Conde-Cid et al., 2018). In the same study, pig slurries featured the highest
number and concentrations of antibiotics. The study of plant uptake of a wide range of
compounds of emerging concern has been recently also reviewed by Pullagurala et al.
(2018) and Madikizela et al. (2018), both high-lightening the role of veterinary medicinal
agents.
Kivits and co-workers (2018) provided also evidence that veterinary antibiotics are
indeed present in groundwater below agricultural areas in the Netherlands and linked this
to the practice of spreading manure.
One of the approaches to reduce possible side effects in manure processing and to
enhance nutrient and resource recovery is the combination of the anaerobic digestion
step with the use of algae in a bio-refinery process (Chowdhury et al., 2018). Manure-
based algae-cultivation hence appears as a cost-effective tool in the management of
digested manure (Deng et al., 2018; Stiles et al., 2018, Renuka et al., 2018). Markou et
al. (2018), however, critically discussed the need to address related contamination risks
and biomass safety concern related in particular to the presence of xenobiotics in the
manure used for these purposes.
While the first related report compiled a series of background information collected, the
results on the analytical characterization of pilot sites operated by the Slovak University
of Agriculture in Nitra are presented and discussed in the following sections. The pilot
exercise aimed at the organisation of larger EU-wide assessment of processed manure,
outline of which is also presented at the end.
Manure samples (processed and untreated), runoff, groundwater and surface water
samples, were analysed for 488 compounds covering typical representatives of
herbicides, fungicides, insecticides, pharmaceuticals, ingredients of personal care
products and other industrially used chemicals. For 60 of these compounds
(corresponding to 12 %), concentration above the established limits of quantification of
these novel multi-compound technique were obtained.
Although this study does not allow characterizing the respective test sites, it delivers an
understanding of environmental pressures created on sites and under real-field
scenarios. The experimental work conducted permits to establish a link between the
evaluation of scientific literature, the biogeochemical modelling and the field conditions
scenarios of when processed manure is applied.
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2. Multi-compound analytical methodology
All samples collected in this study were analysed for compounds listed in Table 1 of the
Annex, including some five hundred chemicals belonging to different categories (i.e.:
industry, personal care, pesticides and pharmaceuticals, etc.).
This selection includes also the compounds considered in the JRC technical Report
"Residues of antimicrobial agents and related compounds of emerging concern in
manure, water and soil", Part I Pilot-sampling campaign in Slovakia and first findings
(Tavazzi et al., 2018).
The following environmental matrices, collected as described in Section 2.1 of Tavazzi et
al., 2018, were processed with ad-hoc developed analytical procedures:
cattle urine,
non-processed manure,
processed manure,
soil,
water samples including groundwater, run-off water and surface water.
2.1.1. Cattle urine for polar compounds extraction
A more specific procedure for urine sample extraction compared to protein precipitation
was developed, which implies the use of SPE cartridge. The rationale was identified in the
possible more accurate interferences' elimination provided by SPE with the aim of
improving the selectivity of the entire procedure.
Cattle urine sample was allowed to equilibrate at room temperature, then vigorously
hand-shaken. A 10 ml aliquot was diluted with 90 ml MilliQ water into a 100 ml glass
bottle. 0.05 ml of internal standard mixtures at 1 µg/ml was added and the sample was
vortex-mixed for 30 seconds.
The diluted sample was then extracted by SPE, using Oasis HBL cartridge (Waters,
Milford, MA, USA), according to the procedure reported in Table 2, Annex I.
A sequential elution was performed with 6 ml ethyl acetate (1st fraction) followed by 6 ml
methanol (2nd fraction). All used solvents were “pesticide analysis” grade.
The two fractions were mixed and evaporated to dryness. The sample was reconstituted
in 0.5 ml reconstituting solution and analysed by UHPLC-MS/MS.
2.1.2. Cattle urine for apolar compounds extraction
Apolar compounds were extracted from cattle urine by Liquid–Liquid Extraction (LLE),
using ethyl acetate as extraction solvent. The procedure was applied to a 20 ml cattle
urine aliquot, opportunely diluted with 20 ml MilliQ water.
A 250 ml Erlenmeyer flask was used for LLE. Diluted sample was spiked with 0.2 ml of
internal standards mixture at 1 µg/ml and then extracted with 50 ml of ethyl acetate,
using a horizontal shaker (speed 110) for 20 minutes. After shaking, the sample was left
to separate into two phases for 30 minutes. The extraction was repeated three times.
The ethyl acetate layer (top layer) was dried onto a 10 g anhydrous Na2SO4 column,
previously prepared. After drying, 0.1 ml of syringe standard solution at 1 µg/ml was
added and the sample evaporated to about 0.5 ml volume. The sample was then
transferred into a 1.5 ml brown MS vial, using toluene and evaporated to a final volume
of 0.2 ml, under gentle stream of nitrogen.
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2.1.3. Non-processed manure for polar compounds extraction
Non-processed manure sample (humid weight 500 g) was freeze-dried using Heto
DRYWINNER equipment, at -60 °C for two days at about 0.1 mmHg pressure.
Solid-Liquid Extraction (SLE) was performed on 1 g aliquot of dried sample using ethyl
acetate/methanol (50:50 %, v/v) as extraction solvent.
Duplicate sample was placed in a 250 ml Erlenmeyer flask, added of 50 ml of extraction
solvent, 0.05 ml of internal standard mixtures at 1 µg/ml and then placed in horizontal
shaker (speed 10) for 20 minutes.
SLE was repeated three times and the extracts were combined and filtered through a
glass-filter frit.
After evaporation to dryness in water-bath at 47°C, the samples were analysed by
UHPLC-MS.
2.1.4. Non-processed manure for apolar compounds extraction
Non-processed manure sample (wet weight 500 g) was freeze-dried using Heto
DRYWINNER equipment, at -60 °C for two days at about 0.1 mmHg pressure, and then
sieved through 2 mm grid.
Solid-Liquid Extraction (SLE) was performed on 1 g aliquot of dried sample using ethyl
acetate as extraction solvent.
The sample was placed in a 250 ml Erlenmeyer flask; after addition of 150 ml of ethyl
acetate and 0.2 ml of internal standard mixture at 1 µg/ml it was placed in horizontal
shaker (speed 10) for 20 minutes.
After shaking, the sample was left to separate into two phases for 30 minutes.
SLE was repeated three times and the extracts were combined and dried onto an
anhydrous Na2SO4 column previously prepared.
After addition of 0.1 ml of syringe standards mixture at 1 µg/ml, the sample was
evaporated to about 0.5 ml volume.
The evaporated extract was finally transferred into a 1.5 ml brown MS vial, using toluene
and the final volume adjusted at 0.2 ml, under gentle stream of nitrogen.
2.1.5. Processed manure
Processed manure samples (i.e.: digestate) were filtered through 5 µm and 1 µm glass-
fibre disk, consecutively.
Solid and liquid fraction underwent different processing.
Polar compounds were extracted from solid fraction by SLE, using Ethyl Acetate:
Methanol 50:50, % v/v as extraction solvent (see Section 2.1.3) and from liquid phase
by SPE (see Section 2.1.1).
Apolar substances were extracted from both fractions by solvent extraction using Ethyl
Acetate as extraction solvent as reported in Sections 2.1.4 and 2.1.2, respectively.
2.1.6. Soil
Soil samples were freeze-dried using Heto DRYWINNER equipment, at -60°C for two days
at about 0.1 mmHg pressure. Before extraction the sample was spiked with 0.2 ml of
semi-volatile internal standard mixture at 1 µg/ml and of 0.05 ml of polar internal
standards mixture.
50 g of freeze-dried sample were extracted in ultrasonic bath for 30 minutes at 40°C
using ethyl acetate and then methanol as extraction solvents. For each solvent, the
extraction was repeated three times. Half of the ethyl acetate fraction was used for semi-
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volatile analysis. The remaining portion was added to the methanolic fraction and used
for polar compounds analysis.
The extracts for semi-volatile analysis were dried onto anhydrous Na2SO4 column
previously prepared. After addition of 0.1 ml of syringe standards mixture at 1 µg/ml, the
samples was evaporated to about 0.5 ml volume, then transferred into a 1.5 ml brown
MS vial, using toluene and the final volume adjusted at 0.2 ml, under gentle stream of
nitrogen.
The extracts for polar compounds analysis were dried onto anhydrous Na2SO4 column
previously prepared. After addition of 0.1 ml of syringe standards mixture at 1 µg/ml, the
samples was evaporated to about 0.5 ml volume, then transferred into a 1.5 ml brown
MS vial, using toluene and the final volume adjusted at 0.2 ml, under gentle stream of
nitrogen.
2.1.7. Water for polar and apolar substances
Extraction of water samples were performed using a JRC in-house developed sampling
device (i.e.: Mariani box) for on-site Solid Phase Extraction (SPE) of environmental water
samples. The procedure is summarised in Table 3 of Annex 1.
HLB SPE Disk (Hydrophilic/Lipophilic Balanced - AtlanticTM HLB-H) filtration/adsorption
disks, previously cleaned and conditioned, were used for sample extraction and
concentration.
Samples were filtered at an average flow of 0.140 l/min, using the transportable field
sampling device developed by JRC (i.e.: Mariani box). Briefly, the device consists of a
Teflon holder for the 47mm SPE Disk, a membrane pump, a digital flowmeter counter
and a battery (12V-9A/h). All spare parts were assembled in an aluminium box, as
depicted in Figure 1.
Figure 1: JRC in-house developed sampling device (i.e.: Mariani box)
The water is sampled from a tube (6) through the pump (4) and organic contaminants are trapped on the filter (1). The flow meter (2, 3) counts the water volume sampled. At the end, the treated water is discharged from tube (7). The pump is powered by a battery (5). The contaminants contained in the filter are further identified and quantified in a specialised laboratory.
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HLB disks’ activation, drying and elution were performed using an automatic extractor
(J2 Scientific).
SPE experimental conditions are summarized in Table 1 of the Annex.
A two fractions sequential elution was performed with 3 x 20 ml ethyl acetate (1st
fraction) followed by 3 x 20 ml methanol (2nd fraction). All used solvents were Pesticide
Analysis grade.
The ethyl acetate fraction was divided into two portions for the apolar and polar
compounds analysis, respectively.
The portion dedicated to apolar analysis was dried onto an anhydrous Na2SO4 column
previously prepared. After addition of 0.1 ml of syringe standards mixture at 1 µg/ml, the
sample was evaporated to about 0.5 ml volume. The evaporated extract was finally
transferred into a 1.5 ml brown MS vial, using toluene. The final volume was adjusted at
0.2 ml, under gentle stream of nitrogen and submitted to HRGC-IonTrap-MS analysis.
The portion dedicated to polar compounds analysis was added to the methanolic eluate,
mixed and evaporated to dryness.
The sample was reconstituted in 0.5 ml reconstituting solution and analysed by UHPLC-
MS/MS.
2.2. Analytical methods
2.2.1. UHPLC-MS/MS
The experimental conditions for polar compounds UHPLC-MSMS analysis are reported in
Table 4 in the Annex. The chromatography was performed in gradient mode according to
the scheme reported in the Table 5 of the Annex.
2.2.1.1. QTRAP 5500 MS/MS operative conditions
An ABSciex QTRAP5500 mass spectrometer equipped with Turbo V™ ion source was used
for polar compounds analysis. The instrument was previously tuned and calibrated in
electrospray mode using PPG's. Prior to analysis all the specific parameters were
optimized infusing a 1 µg/mL standard solution of analytes and internal standards.
The eluate from the column was introduced directly into the ion source. The rapid
desolvatation and vaporization of the droplets minimizes thermal decomposition and
preserves their molecular identity.
The data were collected using the software program Analyst 1.6.2.
All calculations were based on chromatographic peak area ratios for the MRM precursor-
product ion transitions for analytes versus the relative internal standards.
General operating conditions for QTRAP 5500 MS/MS and parameters of the multi-
compound method are reported in Tables 6 of the Annex.
2.2.2. GC-IonTrap-MS
All semi-volatiles pesticides were quantified by isotopic dilution method.
Semi-volatiles pesticides were analysed on HRGC (GC Trace 1310, Thermo Electron,
Bremen, Germany), coupled with a ITQ1100 ion-trap mass spectrometer (Thermo
Electron, Bremen, Germany) operating in the EI-mode at 70 eV and in scan mode
ranging from 50 to 650 m/z.
Pesticides were separated on a 30 m long HP-5ms UI column with 0.25 mm i.d. (inner
diameter) and 0.25 µm film (Agilent J&W, USA).
Gas chromatographic conditions were:
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14
PTV injector with temperature program from 100 to 250 °C at 14.5 °C/s, splitless time 1
min., split flow 50 ml/min., constant flow at 1.0 ml min-1 of He, GC-MS interface at 250
°C and a GC program rate: 100 °C for 1 min., 5 °C min-1 to 320 °C for a final isotherm
of 3 min.
In Table 8 of the Annex extrapolated mass used for the quantification and retention time
for native compounds and internal labelled standards are reported.
2.3. Analytical results
The application of the developed multi-compound methodologies (“Compound Fishing”)
enabled the screening of collected matrices for the presence of about five hundred
different chemicals, selected among the main categories of environmental contaminants.
Common processing methods were applied for the extraction of both semi-volatile and
polar contaminants, with only mandatory differences due to the chemical nature of
contaminants of concern, with the aim of obtaining comparable extracts in terms of
presence of interfering substances.
Indeed, the rationale of the new developed processing methods compared to the initial
extraction procedures reported as first findings in the Report Part I of pilot sampling
campaign (Tavazzi et al., 2018) aimed at improving the experimental conditions for a
more effective chemicals extraction. For instance, in the case of urine and liquid manure
extraction, SPE was preferred to protein precipitation for its more effective matrix
component elimination capabilities.
This screening, being a sort of "chemical fishing" in the multitude of traceable chemicals
coming from industry, agriculture and animal husbandry, allowed the identification of the
presence of several classes of contaminants. The quantification of identified substances
served as a tool for a tentative evaluation of their environmental fate.
Sixty substances out of the about five hundreds listed in Table 1 of the Annex (12%)
were above the limit of quantification of the developed procedures and the positive
values are reported in the tables hereafter.
The experimental limits of quantification for each substance in every environmental
matrix are reported in Tables 9 and 10 of the Annex, for substances detected in GC-
IonTrap-MS and LC-MS/MS, respectively. They were calculated considering signal to
noise ratio of 10:1.
For the substances which have not been detected in any environmental matrix, the
reported LOQ values correspond to the lower level of the calibration curve.
Since concentrations of detected substances in the different environmental matrices have
different unit of measurement (i.e.: ng/l and ng/Kg for liquid and solid matrix,
respectively), all the values are expressed in the following tables as ppt (part per trillion,
10-12), assuming the density of water, urine and liquid manure is 1 g/ml.
2.3.1. Results of cattle urine analysis
In the analysis of cattle urine samples, four fungicide, two herbicide and two antibiotic
residues, belonging to different chemical classes, were detected and quantified.
Azoxystrobin, a systemic fungicide widely used in agriculture providing protection against
many types of crops diseases (Catalá-Icardo et al.,2017), was found at 7 ppt level.
Two triazole fungicides, tebuconazole and tetraconazole, were detected at 43 and 34 ppt
level, respectively.
Thiabendazole, a benzimidazole fungicide and parasiticide able to control helminth
species in livestock (EFSA, 2016), was found at 55 ppt level.
Terbutryn, a selective triazine herbicide used both as pre-emergent and post-emergent
control agent (Alshallash, 2014), was detected at 32 ppt level.
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15
The urea herbicide Isoproturon was found at 5 ppt level.
The insecticide cypermethrin is widely used to control insects in the household and
agricultural fields (Akbar et al., 2015). The compound was measured by GC-IonTrap-MS
and detected in urine sample at level near the LOQ value. To better quantify the
concentration, the sample was analysed by HRGC-HRMS using the instrumental method
described in Mariani et al., 2016. The computed concentration level in the sample was
280 ppt.
The polyether antibiotic monensin, extensively used in dairy to prevent coccidiosis
(Watanabe et al., 2008), was detected at 61 ppt level, and the broad-spectrum
tetracycline antibiotic, oxytetracycline, used in livestock to correct breathing disorders,
was detected at 5191 ppt level. Detailed analytical results are reported in Table 1
Table 1: Results of cattle urine analysis
Analyte Main category Category of Use Concentration (ppt)
Azoxystrobin Pesticide Fungicide 6.96
Tebuconazole Pesticide Fungicide 43.0
Tetraconazole Pesticide Fungicide 33.8
Thiabendazole Pesticide Fungicide 55.2
Isoproturon Pesticide Herbicide 5.32
Terbutryn Pesticide Herbicide 31.7
Cypermethrin (*) Pesticide Insecticide 280
Monensin Pharmaceuticals Antibiotics 61.1
Oxytetracycline Pharmaceuticals Antibiotics 5 191
(*)Cypermethrin concentration value was computed by HRGC-HRMS as described in the text
2.3.2. Results of non-processed manure analysis
Non-processed manure analysis revealed the presence of five residues already detected
in cattle urine.
The concentrations of the systemic fungicide azoxystrobin, of the triazole fungicide
tebuconazole and of the polyether antibiotic monensin in non-processed manure sample
resulted hundreds times higher than in cattle urine, being measured at 7 261, 39 548 and
19 797 ppt level, respectively.
The fungicides tetraconazole and thiabendazole were measured at comparable levels
(i.e.: tetraconazole 64 ppt, thiabendazole 10 ppt) than in cattle urine.
Further to the chemical residues already detected and quantified in cattle urine, non-
processed manure analysis revealed the presence of three additional compounds.
The azole fungicide cyproconazole, widely used on cereal crops (Saraiva et al., 2018),
was found at 270 ppt level; the sulfonamide antimicrobial agent sulfamethazine, usually
administered to animals to prevent infectious diseases (Hirth et al., 2016), was found at
407 ppt level; the organic compound piperonyl butoxide, generally used as a component
in pesticide formulation and acting as an insecticide synergist by inhibiting the natural
defence mechanisms of the insect (Marchand et al., 2017), was found at 4 589 ppt level.
Detailed analytical results of non-processed manure are reported in Table 2.
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16
Table 2: Results of cattle non-processed manure analysis
Analyte Main category Category of Use Concentration (ppt)
Acesulfame K Personal care Sweetening agent 167
Azoxystrobin Pesticide Fungicide 7 261
Cyproconazole Pesticide Fungicide 270
Tebuconazole Pesticide Fungicide 39 548
Tetraconazole Pesticide Fungicide 63.7
Thiabendazole Pesticide Fungicide 9.69
Piperonyl butoxide Pesticide Insecticide, synergist 4 589
Monensin Pharmaceuticals Antibiotics 19 797
Sulfamethazine Pharmaceuticals Antibiotics 407
2.3.3. Results of processed manure analysis
In the analysis of processed manure samples, eleven residues were found, including both
compounds already found in either cattle urine or non-processed manure samples and
compounds not traceable in the previously mentioned matrices.
Concentration of azoxystrobin and tebuconazole decreased after processing to average
values of 437 and 11 534 ppt, respectively, while monensin concentration doubled to
about 37 761 ppt.
Both cyproconazole and piperonyl butoxyde concentrations increased to average values
of about 18 000 ppt, while Sulfamethazine concentration decreased to about 70 ppt.
Additional residues were found, including the systemic fungicides propiconazole and
spiroxamine, detected at average levels of 10 031 and 14 432 ppt, the selective pre-
emergent herbicide oxadiazon and the urea herbicide chlorotoluron detected at average
levels of 959 and 740 ppt, respectively.
The presence of the sweetening agent acesulfame was detected at 47 405 ppt level.
Results of processed manure samples analysis are reported in Table 3 as sum of
concentration found in both solid and liquid fractions. Two samples were collected and
their results are reported individually.
Table 3: Results of cattle processed manure analysis
Analyte Main category Category of Use Concentration
sample 1 (ppt)
Concentration
sample 2 (ppt)
Acesulfame K Personal care Sweetening agent 49 362 45 449
Cyproconazole Pesticide Fungicide 17 930 18 575
Propiconazole Pesticide Fungicide 10 397 9 665
Spiroxamine Pesticide Fungicide 16 444 12 421
Azoxystrobin Pesticide Fungicide 186 687
Tebuconazole Pesticide Fungicide 11 504 11 563
Oxadiazon Pesticide Herbicide 1 714 204
Chlorotoluron Pesticide Herbicide 648 831
Piperonyl butoxide
Pesticide Insecticide, synergist
19 150 16 839
Monensin Pharmaceuticals Antibiotics 42 836 32 685
Sulfamethazine Pharmaceuticals Antibiotics 100 45.6
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17
2.3.4. Results of soil analysis
Soil samples were collected in four different parcels, as indicated in Section 2.2.1 of the
Report Part I of pilot sampling campaign (Tavazzi et al., 2018) and analysed.
The analysis of soil samples revealed the presence of thirty one residues, seven of which
already found in the previously mentioned matrices.
Azoxystrobin was detected only in soil samples from parcel 2 at 1 400 ppt level;
tebuconazole, tetraconazole and thiabendazole concentrations ranged from 15 to 2 353,
145 to 2 301 and from 2 to 39 ppt, respectively.
Cyproconazole and propiconazole were found in all collected parcels and their
concentration ranged from 10 to 3 373 and from 45 to 6 166 ppt, respectively.
Cypermethrin, also detected in the cattle urine sample, was found only in soil samples n°
1 and 7 at a concentration level of 15 500 and 75 500 ppt.
Several residues not previously detected, included:
the bird repellent anthraquinone;
two pre-emergent soil incorporated herbicides (the chloracetanilide herbicide
metolachlor and the dinitroaniline residue, trifluralin);
three contact and residual herbicides (the chloroacetanilide herbicide metazachlor,
the pyridazinone herbicide chloridazon and the urea herbicide chlorotoluron);
the non-selective herbicide prometon;
the diacylhidrazine herbicide methoxyfenozide and the benzofuranyl
methylcarbamate insecticide furathiocarb;
six fungicide residues belonging to different chemical classes (boscalid,
dimoxystrobin, epoxiconazole, metconazole, hexachlorobenzene and
carbendazim)
the neonicotinoid insecticide imidacloprid;
the non-systemic pyrethroid insecticide cypermethrin;
the broad spectrum organophosphate insecticide chlorpyrifos;
the veterinary antifungal antibiotic flusilazole;
the obsolete and banned organochlorine insecticides cis-Chlordane and o,p-DDT
and p,p'-DDT, together with their breakdown products, p,p’-DDE and p,p’ DDD.
Concentrations values of the insecticide p,p’-DDT were reformulated according to
a corrective factor due to a peak overlap with an unknown co-eluting compound.
The two peaks were properly resolved analysing the samples using HRGC-HRMS.
The correction procedure is explained in Section 2.4.2. For this reason, p,p'-DDT
concentrations should be considered only as indicative of the presence of the
compound in soil samples. Detailed analytical results are reported in Table 4.
Table 4: Results of soil analysis
Analyte Main category Category of Use
Soil sample (ppt)
Parcel 1
Soil sample (ppt)
Parcel 2
Soil sample (ppt)
Parcel 3
Soil sample
(ng/kg) Parcel
7
Anthraquinone Pesticide Bird repellent 2215 5660 2464 4797
cis-Chlordane Pesticide Insecticide
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18
Analyte Main category Category of Use
Soil sample (ppt)
Parcel 1
Soil sample (ppt)
Parcel 2
Soil sample (ppt)
Parcel 3
Soil sample (ng/kg) Parcel
7
p,p'-DDD Pesticide Breakdown product 157 500 2038 1742
Cyproconazole Pesticide Fungicide 10 59 82 3373
Propiconazole Pesticide Fungicide 45 6166 260 342
Boscalid Pesticide Fungicide 865 374
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19
Forty chemical residues were quantified in water samples, including five chemicals
originating from the industrial sector (corrosion inhibitor and surfactants), ten herbicides,
eight fungicides and seven insecticides, nine pharmaceuticals and one personal care
product.
Detailed analytical results are reported in Table 5.
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20
Table 5: Results of water samples analysis
Analyte Main category Category of
Use
Ground Water (ppt)
Runoff Water (ppt)
Runnoff. Loc A (ppt)
Runnoff. Loc B (ppt)
Stream oponice 50
m (ppt)
Strem oponice 25
m (ppt)
Nitra River near pumphouse
(ppt)
Benzotriazole Industry Corrosion Inhibitor
76.9 2.08 2.30 2.77 296 319 220
PFHpA Industry Surfactant 1.09 0.434 0.542 0.380 2.07 1.52 0.380
PFHxA Industry Surfactant 1.92 1.48 0.946 0.715 2.09 2.40 1.13
PFNA Industry Surfactant 0.864 0.290 0.595 0.470 1.41 0.972 0.582
PFOA Industry Surfactant 2.14 0.927 0.704 0.937 1.88 1.40 1.23
Acesulfame K Personal care Sweetening
agent 29.3 26.0 30.4 21.1 31.3 19.5 31.3
Cyproconazole Pesticide Fungicide 0.667 1.07 0.664 0.735 1.31 0.970 0.533
Propiconazole Pesticide Fungicide 0.837 2.37 1.14 0.830 0.789 2.24 0.842
Azoxystrobin Pesticide Fungicide
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21
Analyte Main category Category of
Use
Ground Water (ppt)
Runoff Water (ppt)
Runnoff. Loc A (ppt)
Runnoff. Loc B (ppt)
Stream oponice 50
m (ppt)
Strem oponice 25
m (ppt)
Nitra River near pumphouse
(ppt)
Imidacloprid Pesticide Insecticide 0.403
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22
2.4. Discussion
2.4.1. Comparison between LC-MS/MS and GC-IonTrap-MS
From the total list of about 500 chemicals, 33 of them have been analysed by both GC-
IonTrap-MS and LC-MS/MS methodologies. All of the 33 common chemicals are
pesticides.
As reported in Table 6, the majority of common chemicals were found below the LOQ by
both techniques. Six out of 33 compounds were only detected by LC-MS/MS, while two of
them were detected with both techniques. A single analyte was detected by Ion Trap GC-
MS only.
Indeed, the higher sensitivity of LC-MS/MS compared to GC-IonTrap-MS offers the
advantage of reaching lower concentration values.
In particular:
- Metolachlor: it was detected in water and soils samples with both techniques.
Concentrations in GC-IonTrap-MS were from 1.3 up to 4 times higher than in LC-
MS/MS. This substantial difference occurred when measured concentrations were
closed to the LOQ of GC-IonTrap-MS method. Concentrations reported in tables
referred to LC-MS/MS analysis.
- Tebuconazole: it was detected in water, urine, non-processed manure, processed
manure and soil samples by LC-MS/MS. In GC-IonTrap-MS, its presence was
observed only in some water samples, but the resulting concentrations were
below the LOQ. Concentrations reported in tables referred to LC-MS/MS analysis.
Tebuconazole presence in the samples was also confirmed by HRGC-HRMS.
- Flusilazole: comparable concentrations were detected by both analytical
techniques. Concentrations reported in tables referred to GC-IonTrap-MS analysis.
Table 6: List of common compound analysed with both analytical methodologies
Compound GC-IonTrap-MS LC-MS/MS Comments
Alachlor
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23
Compound GC-IonTrap-MS LC-MS/MS Comments
Fluquinconazole
-
24
Compound GC-IonTrap-MS LC-MS/MS Comments
Triadimenol
-
25
- Chlorpyrifos: detected and quantified in all samples. The concentration computed
by HRGC-HRMS were, in average, 4 times higher than the values obtained by GC-
IonTrap-MS;
- Hexachlorobenzene: confirmed in all soil samples;
- Heptachlor epoxide: detected in all soils samples (not identified by Ion trap GC-
MS);
- cis-Chlordane: detected in all soil samples (detected by GC-IonTrap-MS in only
one soil sample, parcel 3);
- Trifluralin: confirmed in all soil samples;
- DDD: confirmed in all soil samples;
- DDE: confirmed in all soil samples;
- DDT: confirmed in all soil samples. However, its measured concentrations were
very high compared to its metabolites, DDE and DDT, supposing, at the
beginning, a recent use of DDT. For this reason, a dedicated discussion follows
below, with details on p,p’-DDT quantification using a corrective factor.
2.4.3. HRGC-HRMS confirmation analysis of GC-Ion Trap-MS results
As mentioned before, very high suspected concentrations of p,p’-DDT in soils, requested
a deeper investigation in order to confirm the results .
A further analysis was carried out by HRGC-HRMS using two capillary columns with
different polarity phases:
1. 60 m. long HP-5ms UI column with 0.25 mm i.d. and 0.25 µm film (Agilent J&W,
USA);
2. 60 m long HT8 column with 0.25 mm i.d. and 0.25 µm film (SGE Analytical
Science, Australia).
In HRGC-HRMS, both columns highlighted two closed but well separated peaks. In Figure
2B, using the HP5 ms column 60 m, a first peak was detected at the retention time (RT)
of 20.66 min, which corresponds to p,p’-DDT, and a second peak at RT=20.84 min which
is compatible with a different compounds. Similar results were obtained on HT8 column
(chromatogram not reported). This suggest the coelution of p,p’-DDT with another
chemical with similar mass spectrum, when using a shorter GC column (HP-5 ms, 30 m)
on GC-Ion Trap-MS. In Figure 2A, a coeluted peak was detected at the RT of 27.6 min,
which correspond to the overlap between p,p’-DDT and the unknown.
In Figure 3A, the mass spectrum of p,p’-DDT is reported; in Figure 3B the mass
spectrum of the unknown, detected in soil samples, is given. The two spectra showed a
similar pattern, sharing the ions m/z 235 and 237 used for the quantification of DDT. The
unknown is characterised by the different ions m/z 164 and 200, instead of ions m/z 165
and 199 which are typical for DDT.
In Figure 4 the list of the first possible candidates for the unknown, extracted from the
search tool of the NIST library, is given. Several compounds show a very similar mass
spectrum to DDT, having as main masses the ions m/z 235 and 237. However, all listed
candidates, missed both the ion m/z 164 and 200 which are typical for the unknown.
Given the existence of a coeluted compound in GC-IonTrap-MS, which interfere with the
p,p’-DDT peak, the concentration of p,p’-DDT was corrected using a correction factor.
This correction factor (CF) was evaluated for each sample respectively, by computing the
ratio between the p,p’-DDT area and the sum of p,p’-DDT and unknown compound areas
in the peaks identified in HRGC-HRMS. Then, the area of the peak identified in GC-
IonTrap-MS was multiplied by the CF to remove the influence of the coeluted unknown.
For this reason, the concentration for the compound p,p’-DDT should be considered as
indicative only
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26
Figure 2: p,p’-DDT peak in GC-IonTrap-MS (A, HP-5ms 30 m) and HRGC-HRMS (B, HP-
5ms)
Figure 3: sample spectra of p,p’-DDT (A) and the coeluted unknown (B)
A B
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27
Figure 4: list of possible candidates for the unknown, obtained from the NIST library
Substance name Library spectra
Name: 1,2-Bis(2.chlorophenyl)-1,2-bis(3-chlorophenyl)ethane
Formula: C26H18Cl4
Exact mass: 470.016262
Synonyms:
1.1-chloro-2-[1,2,2-tris(3-chlorophenyl)ethyl]benzene
Name: 9H-Carbazole, 3,6-dichloro-
Formula: C12H7Cl2N
Exact mass: 234.995555
Synonyms: Carbazole, 3,6-dichloro-; 3,6-Dichlorocarbazole
Name: 3-Butanone,1,1-bis(4-chlorophenyl)-2,2-dimethyl-
Formula: C18H18Cl2O
Exact mass: 320.073470
Synonyms: 4,4-Bis(4-chlorophenyl)-3,3-dimethyl-2-butanone
Name: 2,2-Bis-(4-chlorophenyl)acetic acid
Formula: C14H10Cl2O2
Exact mass: 280.005785
Synonyms: p,p'-DDA; DDA; Dichlorodiphenylacetic acid
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28
2.4.4. Observed occurrences and levels
In order to easily summarise, compare and discuss the concentration values found in the
different matrices of collected samples, a graphical representation was provided.
Since concentrations of detected compounds in the different environmental matrices
have different unit of measurement (i.e.: ng/L and ng/Kg), all the values are expressed
in the following charts as ppt (part per trillion, 10-12), assuming the density of water,
urine and liquid manure is 1 g/ml.
Concentrations of detected compounds show a huge variation in values (from few ppt to
ppb) depending on the type of the measured matrix. In order to represents concentration
values, which differs up to 7 orders of magnitude in the same chart, 5 ranges of
concentration have been selected. Ranges of concentrations, which are also reported in
the legend of each chart, are:
0 to 1 ppt
1 to 10 ppt
10 to 100 ppt
100 to 1 000 ppt
1 000 to 10 000 ppt
higher than 10 000 ppt.
Moreover, to condense and make more readable the quantity of information available,
some samples were grouped together according to the following computations:
Processed manure: the concentrations found in the two liquid and solid extracts
were summed up to consider the total content in the original manure samples.
The two summed samples were then averaged.
Runoff: samples from location A and B were averaged, since they were collected
in the same field
Non processed manure: the two collected samples, sampled from the same
manure batch, were averaged.
Labels for the x-axis of the following charts have been provided according to the matrix
of the sample and to the groups of samples described above:
URINE: cattle urine sample
MAN: non processed manure samples
Proc.MAN: processed manure samples;
SOIL: soil samples. The number indicates the soil parcel;
RUNOFF: runoff between parcels 2 and 4;
RUNOFF.AB: two runoff samples from the same parcel 7;
RIVER.50: surface water at 59 m from the river insertion;
RIVER.25: surface water at 25 m from the river insertion;
RIVER: surface water near the pump-house;
GWW: groundwater sample.
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29
To improve the visualisation of the heterogeneous information, coloured boxes highlight
the different matrices involved in the campaign:
YELLOW: urine samples
BROWN: manure samples (both processed and non-processed)
GREEN: soil samples
BLUE: water samples, including runoff, surface water and groundwater.
The observed occurrences and levels of detected substances are reported in Figure 5 to
Figure 10. Hereafter follow a description of the results for each chemical category.
Fungicides (Figure 5)
Five fungicide residues (i.e.: thiabendazole, tetraconazole, tebuconazole, cyproconazole
and azoxystrobin) were found in urine, non-processed manure, soil and water samples
(even including groundwater). These findings suggest the application of urine and non-
processed manure used in agriculture as potential source of environmental
contamination. A further fungicide, tricyclazole, was detected in water samples only.
Seven fungicide residues (i.e.: metconazole, hexachlorobenzene, flusilazole,
epoxiconazole, dimoxystrobin, carbendazim and boscalid) were found only in soil
samples, except epoxiconazole found also in one Nitra river water sample.
Herbicides (Figure 6)
Two herbicides (i.e. isoproturon and terbutryn) were found in urine and some water
samples, but not in soil. Prometon and metazachlor were found in soil samples, only,
while metolachlor, chlorotoluron and chloridazon were found both in soil and water
samples. Five herbicide residues (i.e.: Secbumeton, fluometuron, diuron, 2,4,5-T and
2,4-D) were found in water samples, only.
Insecticides (Figure 7)
Cypermethrin was found in urine and soil samples while piperonyl butoxide was detected
in manure and water samples. Methoxyfenozide and imidacloprid were found in soils and
waters samples. Chlorpyrifos was found in soil and groundwater.
Furathiocarb was detected only in soil sample from parcel 1. Prometryn and clothianidin
were found in waters and metaflumizone only in one river Nitra sample.
Pharmaceuticals & Personal care products (Figure 8)
Oxytetracycline was found in urine sample only. Monensin was found in urine, manure
and in all water samples, but not in soils. Sulfamethazine was found in manure, Nitra
river and in groundwater samples. Sulfamethoxazole was found in all water samples
while its metabolite, N-acetyl-sulfamethoxazole, was detected only in three river
samples. Oxolinic acid was detected in run-off and groundwater samples and
clarithromycin was found in two river water samples. Two antifungal residues were
found: fluconazole was detected in two soil parcels and in all water samples, while
climbazole was found only in one Nitra river water sample. The presence of anti-
inflammatory drug diclofenac was detected in all three Nitra river water samples. The
sweetening agent acesulfame K was detected in manure and all analysed water samples.
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30
Industry (Figure 9)
Four perfluoroalkyl acids (i.e.: perfluorohexanoic acid perfluoroheptanoic acid
perfluorooctanoic acid and perfluorononanoic acid) as well as the heterocyclic compound
benzotriazole were found in all water samples.
Banned pesticides and bird repellent (Figure 10)
The bird repellent Anthraquinone and background levels of DDTs were found in all soil
samples. Very low level of Cis-Chlordane was found in soil sample from parcel 3.
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31
Figure 5: concentrations of fungicides detected in collected samples
-
32
Figure 6: concentrations of herbicides detected in collected samples
-
33
Figure 7: concentrations of insecticides detected in collected samples
-
34
Figure 8: concentrations of pharmaceuticals and personal care products detected in collected samples
-
35
Figure 9: concentrations of industrial products detected in collected samples
-
36
Figure 10: concentrations of banned pesticides and bird repellent detected in collected samples
-
37
2.4.5. Qualitative PCA
The application of the principal component analysis (PCA) technique to the dataset, was
tentatively done to deduce some additional information. Usually, PCA is carried out to
reduce the dimensionality of chemical datasets of different nature, in order to identify
potential sources of anthropogenic/natural contamination.
However, to obtain stable results using PCA, the number of samples must be sufficiently
greater respect to the number of variables (in this case, variables are the single detected
compounds). Moreover, in order to avoid distortion of results, each variable should have
a low proportion of data below the limit of detection/quantification.
In this specific case, the number of samples is lower compared to the number of detected
compounds and the data below the limit of quantification ranges between 25% and 80%.
A tentative PCA application was however carried out using R software (R version 3.5.0; R
Core Team, 2018). Prior to the PCA computation, individual variables measured below
quantification limits were replaced by a value closed to zero and a data standardisation
procedure was applied in order to avoid the greatest values to have the greatest
influence on the analysis.
All single samples were used for the analysis, with an exception for processed manure
samples: concentrations in liquid and solid extracts were summed up because the
original samples was collected as a mix of solid and liquid manure. It was also decided to
remove all detected chemicals stemming from industrial activities, since they were
detected in water samples only.
Due to the complexity of different matrices, PCA was applied on separated subsets:
- Water, urine and manure samples;
- Water and soil samples;
- Soil, urine and manure samples.
Results from PCA were quite limitative due to the number of approximations given for the
number of samples vs variables, and for the high percentage of concentrations below the
limit of quantification. However, two general conclusions resulting from a first analysis,
and visualised in PCA charts of Figure 11 and Figure 12, could be drawn:
Processed manure and non-processed manure were not identified by PCA to have
a common origin. Indeed they were collected in different time period and from a
different batch of manure and should be treated as different samples;
Urine and non-processed manure can be treated as a common source for some of
the detected compounds.
A further PCA run was then carried out removing processed manure samples from the
dataset. This analysis seems to show that the use of urine and manure as fertiliser is a
potential source of contamination of waters. The analysis also suggest river water to
represent a different group of compounds stemming from other source(s) and/or
application(s). In case of soils, PCA analysis did not reveal a particular trend with other
matrices, because a lot of pesticides were detected only in soils and thus resulting as a
confounding element in PCA analysis.
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38
Figure 11: PCA chart from analysis of manure, soil and urine samples
Figure 12: PCA chart from analysis of manure, water and urine samples
-
39
3. Design for an EU-wide assessment
The afore-described experiments demonstrate the applicability of what we like to call
"Compound-fishing concept", i.e. the hybrid approach between a targeted analyses and
non-targeted methodologies. The combined assessment of manure, processed manure
and exposed water and soil is possible with this methods and can be combined with the
assessment of agronomic parameters such as the content of nitrogen and its various
forms, the content of phosphorous and other properties relevant to characterise manure
and its derived materials as fertilizers.
The relevant environmental processes to be addressed are still complex and it goes
without saying that a full experimental characterisation of all processes in all possible
cases and scenarios is not possible.
In order to obtain a synergy between the modelling assessments carried out by other
groups and the "on site" reality the experimental set-up has to address the following
issues regarding the relationship between processed and unprocessed manure materials,
their field behaviour in particular also in regard to envisaged definition of safe manure
criteria.
3.1. Material characterisation
This includes a direct comparison (where possible) between processed and unprocessed
manure. Although, as shown in Figure 13, a variety of processing technologies are
known, the focus will be on samples from materials being of interest for a mineral
fertiliser equivalence.
In addition to the information retrieved from the providers the following parameters will
be tested for:
Agronomic value:
o Mineral-N (NH4+, NO3-)
o Organic nitrogen
o P-content (and fractionation)
o K-content
o Other micro-nutrients
o Dry-matter content
o Ash content (loss-on-ignation)
Environmental risks:
o Veterinary medicinal agents including antibiotics
o Pesticides
o Heavy metals
o Persistent organic pollutants (Polyaromatic hydrocarbons, poly-
chlorinated biphenyls)
o Others
The respective measurements of agronomic value related properties will be outsourced,
requiring compliance to ISO 17025 standard and the use of CEN/ISO Standards were
applicable.
For the measurement of the organic pollutants the "compound fishing approach" will be
used and enlarged to cover some 700 chemical substances. It is expected to perform
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40
these tests on selection of relevant candidate materials and its original manure matrix at
a total of ca. 25-30 materials. The candidate materials are currently identified through an
open call and will include representatives of all major processing technologies shown in
the figure below.
Since the scope of this exercise is NOT to assess the performance of specific manure
process plants in terms of variability and homogeneity of the quality of the delivered
materials, simple grab samples from the normal production process are sufficient for the
scope of the exercise. Generally, sampling approaches should comply with the
requirements defined by the Sampling Framework established through CEN TC 292.
Figure 13: Main processing technologies for manure
3.2. Environmental behaviour
Within this framework, the role of agricultural application of manure (processed or not) in
the propagation of anti-microbial resistance (AMR), interspecies exchange and antibiotic
resistant genes as well as the role of veterinary antimicrobial agents and chemicals
related to animal husbandry will be addressed in conjunction with the release of nitrogen
species. Indeed, there is a significant data gap on the drainage of nutrients from lands
that have been irrigated with treated wastewater or that have been fertilised directly with
animal manure or derived biosolids (e.g. after digestion or further processing). Polluted
runoff, caused by rainfall, snowmelt or irrigation, moves over and through the ground
and carries natural and man-made pollutants that can potentially reach surface water
and underground sources of drinking water. This aspect will therefore need to address
the seasonality of fertiliser application as well as the different pathways. Although this
study does not aim to characterise the respective test sites completely, it will deliver an
understanding of environmental pressures created on sites and under real-field
scenarios. It is envisaged to perform this in autumn and spring at up to five locations
where appropriate candidate materials are used.
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41
4. Conclusions
The applicability of the novel multi-compound analytical method "Compound
fishing" as hybrid method, with an analytical performance ranging between and
combining the advantages of classical target analysis and non-targeted screening
could be demonstrated for the application to manure, treated manure (liquid and
solid), soil as well as agricultural runoff, surface and ground water. This allow to
better address the so-called “Circularity of Risks” stemming from the material
reuse and recycling philosophy under a Circular Economy.
Manure samples (processed and untreated), runoff, groundwater and surface
water samples, were analysed for 488 compounds covering typical representatives
of herbicides, fungicides, insecticides, pharmaceuticals, ingredients of personal
care products and other industrially used chemicals. For 60 of these compounds
(corresponding to 12 %), concentration above the established limits of
quantification of these novel multi-compound technique were obtained.
The sampling of environmental water samples (run-off, groundwater and surface
water) was successfully done using the MARIANI-Box, thus emphasising once
more the validity of the box of environmental organic analyses.
Matrices derived from animal husbandry contained significant levels of veterinary
medicinal agents or chemicals used in animal farming. Quantifiable concentrations
were found for:
o Cattle urine samples for four fungicides, two herbicides and two antibiotic
residues
o Five of the detected residues were also present in the related non-
processed manure. In addition acesulfame K as well 3 other substances
were identified and quantified
o The analyses of processed manure revealed the presence of 11 residues,
including those found previously. While concentrations of two compounds
(azoxystrobin and tebuconazole) decreased, monensin concentration
doubled. Although this is no direct proof, it indicates that some compounds
may disappear upon treatment of manure, while others persist.
o The soil measurements revealed – as expected – the presence of
significantly larger number of chemicals (in total 31), indicating also that
transfer from manure to soil is taking place in some cases.
o Water samples revealed an even higher number of quantifiable compounds
– forty compounds could be identified, which - although not all of them can
be linked to manure – proofs a high vulnerability of exposed waters.
A design for an EU-wide assessment of (processed) manure and an approach to
investigate possible transfer pathways for relevant compounds was identified and
is put into practice. The exercise will be accompanied by a characterisation of
agronomic parameters.
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42
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